{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:31.400499Z",
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     "shell.execute_reply": "2025-02-07T13:59:31.402233Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.400477Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:31.403569Z",
     "iopub.status.busy": "2025-02-07T13:59:31.403235Z",
     "iopub.status.idle": "2025-02-07T13:59:31.411763Z",
     "shell.execute_reply": "2025-02-07T13:59:31.411269Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.403547Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:31.412707Z",
     "iopub.status.busy": "2025-02-07T13:59:31.412336Z",
     "iopub.status.idle": "2025-02-07T13:59:31.414928Z",
     "shell.execute_reply": "2025-02-07T13:59:31.414383Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.412687Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-07T13:59:31.459010Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.415731Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 964421.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:31.460456Z",
     "iopub.status.busy": "2025-02-07T13:59:31.460176Z",
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     "shell.execute_reply": "2025-02-07T13:59:31.469219Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.460435Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
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     "iopub.status.busy": "2025-02-07T13:59:31.471828Z",
     "iopub.status.idle": "2025-02-07T13:59:31.474441Z",
     "shell.execute_reply": "2025-02-07T13:59:31.473957Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.472168Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:31.475470Z",
     "iopub.status.busy": "2025-02-07T13:59:31.475072Z",
     "iopub.status.idle": "2025-02-07T13:59:31.482042Z",
     "shell.execute_reply": "2025-02-07T13:59:31.481449Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.475449Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
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   "id": "71ae51fb9433db98",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
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   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
   "cell_type": "code",
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   "id": "847a70137968848a",
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:31.758760Z",
     "iopub.status.busy": "2025-02-07T13:59:31.758570Z",
     "iopub.status.idle": "2025-02-07T13:59:31.869826Z",
     "shell.execute_reply": "2025-02-07T13:59:31.869265Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.758740Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:31.870689Z",
     "iopub.status.busy": "2025-02-07T13:59:31.870483Z",
     "iopub.status.idle": "2025-02-07T13:59:31.902937Z",
     "shell.execute_reply": "2025-02-07T13:59:31.902348Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.870668Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:31.903848Z",
     "iopub.status.busy": "2025-02-07T13:59:31.903585Z",
     "iopub.status.idle": "2025-02-07T13:59:31.919278Z",
     "shell.execute_reply": "2025-02-07T13:59:31.918717Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.903826Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4540, 0.3600, 0.1861],\n",
      "        [0.2042, 0.0156, 0.7801]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:31.920133Z",
     "iopub.status.busy": "2025-02-07T13:59:31.919933Z",
     "iopub.status.idle": "2025-02-07T13:59:32.181696Z",
     "shell.execute_reply": "2025-02-07T13:59:32.181040Z",
     "shell.execute_reply.started": "2025-02-07T13:59:31.920113Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:32.182975Z",
     "iopub.status.busy": "2025-02-07T13:59:32.182535Z",
     "iopub.status.idle": "2025-02-07T13:59:32.432801Z",
     "shell.execute_reply": "2025-02-07T13:59:32.431574Z",
     "shell.execute_reply.started": "2025-02-07T13:59:32.182948Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
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   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
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    "execution": {
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   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
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   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
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     "shell.execute_reply.started": "2025-02-07T13:59:32.465118Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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1a9bodH13d3dMmjQJkyZNQmFhIZ544gnMnTtXqyRWX1u2bEG7du2wdetWtb/QkpKSquxrbW2N8PBwhIeHQ6lUYtKkSfjkk0+QmJiI9u3bq/Z788030b59e8ycORNOTk6YMWOGVrH06tULBw4cQNu2beHv7w8HBwf4+fnByckJu3fvRnZ2dpUvB33y9vaGEAJt27ZV/cFSF/7+/ti/fz9KSkrUBndUdvT7+/vrK9QqWrRoAQcHBygUilo/v3X5ndeFt7c33n77bbz99ts4e/Ys/P39sXjxYqxfv77aY5ydnTU2BV68eFGnWHSxZcsW9OvXD59++qla+a1bt6q0bNjZ2WHkyJEYOXIkysvLMXz4cMydOxcJCQlqUzoWLlwIKysrTJo0CQ4ODhgzZoxWsZSXlyO/UIHcY23g6KBbb1HJbSXaBlxEXl6e2udTH7UwAHBzc6sy2rKgoACOjo5a18IAjk7USUREBA4fPqxxNM2tW7dw//59AA/+mpPJZGp/LV64cEGrIaiaKBQKFBcXq5W5uLjAw8NDY/OUPlX+ZfrwX6K//PJLlXbsvzdBWlhYqP6y1xRjYmIipk6dioSEBKxYsUKrWHr16oULFy5g8+bNquZFCwsL9OzZE6mpqaioqKjSH6ZPw4cPh6WlJT744IMqf9kLIar8DP7uhRdegEKhUGvOKysrw5o1axAcHFylSVCfLC0tMWLECHz55Zc4ceJElfevX7+uti9Q++9cW3fv3lU1mVby9vaGg4NDrZ9fb29vnDp1Si2+3377rcZRr4+apaVlld//v/71ryq17L9/HqytrdG5c2cIIaqMlpTJZFi5ciVeeOEFREVFYceOHXWKyc5ePxsAODo6qm36SmI9evRARkaGWtnevXvRo0ePOp1H8jWxpUuX4tatW6oBDl9//TUuX74M4MFf+JV9AY/CtGnTsGPHDgwZMgQvv/wyAgICUFpaiuPHj2PLli24cOECmjdvjsGDByM1NRUDBw7EmDFjUFhYiGXLlqF9+/Z17ocBHswRa9WqFV544QX4+fnB3t4e+/btw5EjR6rM69K3IUOGYOvWrXj++ecxePBg5ObmIj09HZ07d1ZrdnjllVdw48YNPP3002jVqhUuXryIjz/+GP7+/nj88cc1nnvhwoUoLi5GTEwMHBwc1AZfaFKZoE6fPo158+apynv37o1du3ap5gU9Kt7e3pgzZw4SEhJw4cIFDBs2DA4ODsjNzcW2bdvw2muvqSYxaxIcHIwXX3wRCQkJKCwsRPv27bFu3TpcuHChyl/1j8L8+fOxf/9+BAcH49VXX0Xnzp1x48YNZGdnY9++fbhx4wYA7X/n2jpz5gz69++PiIgIdO7cGVZWVti2bRsKCgrUBqloMn78eKSmpiIsLAwTJkxAYWEh0tPT4ePjg5KSknr9HHQ1ZMgQzJ49G9HR0ejZsyeOHz+ODRs2VOmfHjBgANzc3BASEgJXV1ecPHkSS5cuxeDBg+Hg4FDlvBYWFli/fj2GDRuGiIgIfPvtt3j66acb6rbq7M6dOzh37pzqdW5uLnJyctC0aVO0bt0aCQkJuHLlCj777DMAD6YJLV26FO+88w7Gjx+P77//Hl988YXGaUc1qtNYRiPUpk0bAUDjlpubW+OxUVFRws7Orkp5nz59NA6FbdOmjRg8eLBa2e3bt0VCQoJo3769sLa2Fs2bNxc9e/YUixYtEuXl5ar9Pv30U9GhQwchl8tFp06dxJo1a6oM2RbiwdBUTUPn27RpoxrqXlZWJqZNmyb8/PyEg4ODsLOzE35+fmL58uU13q8Q6kN46/OzUCqVYt68eaJNmzZCLpeLbt26iZ07d1YZ4rxlyxYxYMAA4eLiIqytrUXr1q3F66+/Lq5du6ba5+Eh9pUUCoUYPXq0sLKyEtu3b6/1flxcXAQAUVBQoCo7dOiQACB69epV6/08fP+ahmjXNMS+0pdffimeeuopYWdnJ+zs7ESnTp1ETEyMOH36dK3x//XXX2Lq1KnCzc1NyOVy0b17d7F79+5aj6vpXjR9ToXQ/NkqKCgQMTExwtPTUzRq1Ei4ubmJ/v37i5UrV6r20fZ3Xvkz0zR0Hg8NSy8qKhIxMTGiU6dOws7OTjg5OYng4GDxxRdfaHXf69evF+3atRPW1tbC399f7Nmzp96xCKH798C9e/fE22+/Ldzd3YWtra0ICQkRhw8frjId4JNPPhG9e/cWzZo1E3K5XHh7e4tp06aJ4uJi1T6a/v+8e/eu6NOnj7C3t1ebNqFJcXGxACDyT7cWd6966bTln24tAKjFV5P9+/dr/B6u/N6KioqqMj1i//79wt/fX1hbW4t27drVaYpUJZkQdezNJiIio1RSUgInJydcPd1KL31iHh0vo7i4uMqEfGPCPjEiIpIsyfeJERGROoUQUOjYyKbr8Q2FSYyIyMQoIaCEbklI1+MbCpsTiYhIslgTIyIyMUoIKMykJsYkRkRkYticSEREJAGsiRERmRhzGp3ImhiAZcuWwcvLCzY2NggODkZWVpbBYklOTkb37t3h4OAAFxcXDBs2TLVmlrGYP38+ZDIZ4uLiDBrHlStX8NJLL6FZs2awtbVFly5dcPToUYPGpFAokJiYiLZt28LW1hbe3t748MMP6/yEfF3Utky8EAIzZ86Eu7s7bG1tERoairNnzxospoqKCkyfPh1dunSBnZ0dPDw8EBkZWe+18vQR099NnDgRMplMbcVrY6bU0yYFZp/ENm/ejPj4eCQlJSE7Oxt+fn4ICwtTW0upIf3www+IiYnBzz//jL1796KiogIDBgxAaWmpQeL5uyNHjuCTTz5RPczXUG7evImQkBA0atQIu3btwh9//IHFixcbfPHAlJQUrFixAkuXLsXJkyeRkpKCBQsW4OOPP26wGGpbJn7BggX46KOPkJ6ejl9++QV2dnYICwur8lDehorp7t27yM7ORmJiIrKzs7F161acPn0azz333COLp7aYHrZt2zb8/PPPWi2NYywU/x3YoesmCXV+UJWJCQoKUnuenEKhEB4eHiI5OdmAUf1PYWGhACB++OEHQ4cibt++LTp06CD27t0r+vTpI6ZMmWKwWKZPny6eeuopg12/OoMHDxbjx49XKxs+fLgYO3asQeLB35aJVyqVws3NTe2Zgrdu3RJyuVx8/vnnBolJk6ysLAFAXLx40aAxXb58WbRs2VKcOHFCtGnTRvzjH/9okHjqq/LZif8+6SIuXXbTafv3SZc6PTvRUMy6JlZeXo5jx46pradkYWGB0NDQei8zoW+VS640bdrUwJEAMTExGDx4cK3rTzWEHTt2IDAwEC+++CJcXFzQrVs3rFq1ytBhoWfPnsjIyMCZM2cAPFgm5NChQ490jbe6yM3NRX5+vtrv0MnJCcHBwUbzmQcefO5lMhmaNGlisBiUSiXGjRuHadOmwcfHx2Bx1IdC6GeTArMe2FFUVASFQqFxiexTp04ZKKr/USqViIuLQ0hICHx9fQ0ay6ZNm5CdnY0jR44YNI5K58+fx4oVKxAfH493330XR44cweTJk2FtbV3tSsUNYcaMGSgpKUGnTp1gaWkJhUKBuXPnYuzYsQaL6WH5+fkAoPEzX/meod27dw/Tp0/H6NGjDfrg2ZSUFFhZWWHy5MkGi6G+9NGnJZU+MbNOYsYuJiYGJ06cwKFDhwwaR15eHqZMmYK9e/eqrT5rSEqlEoGBgap1xLp164YTJ04gPT3doEnsiy++wIYNG7Bx40b4+PggJycHcXFx8PDwMGhcUlFRUYGIiAgIIbReHPVROHbsGJYsWYLs7Gy11azJ+Jh1c2Lz5s1haWmpcYlsNzc3A0X1QGxsLHbu3In9+/ejVatWBo3l2LFjKCwsxBNPPAErKytYWVnhhx9+wEcffQQrKyu1Fasbiru7Ozp37qxW9vjjj+PSpUsNHsvDpk2bhhkzZmDUqFHo0qULxo0bh7feegvJyckGjatS5efaGD/zlQns4sWL2Lt3r0FrYQcPHkRhYSFat26t+sxfvHgRb7/9Nry8vAwWl7aUkEGh46aENJK3WScxa2trBAQEqC2RrVQqkZGRUeclsvVFCIHY2Fhs27YN33//Pdq2bWuQOB7Wv39/HD9+HDk5OaotMDAQY8eORU5Ojmr5+oYUEhJSZerBmTNn0KZNmwaP5WF3796FhYX6/1aWlpZQKo2jcaZt27Zwc3NT+8yXlJTgl19+MdhnHvhfAjt79iz27duHZs2aGSwWABg3bhx+//13tc+8h4cHpk2bhj179hg0Nm0ohX42KTD75sT4+HhERUUhMDAQQUFBSEtLQ2lpKaKjow0ST0xMDDZu3IivvvoKDg4Oqn4KJycn2NraGiQmBweHKn1ydnZ2aNasmcH66t566y307NkT8+bNQ0REBLKysrBy5UqsXLnSIPFUCg8Px9y5c9G6dWv4+Pjg119/RWpqKsaPH99gMdS2THxcXBzmzJmDDh06oG3btkhMTISHhweGDRtmkJjc3d3xwgsvIDs7Gzt37oRCoVB97ps2bQpra+sGj6l169ZVEmmjRo3g5uaGjh07PpJ4qJ4MPTzSGHz88ceidevWwtraWgQFBdW6BPijBA3LewOo17Ldj5Khh9gLIcTXX38tfH19hVwuF506dRIrV640aDxCCFFSUiKmTJkiWrduLWxsbES7du3Ee++9J8rKyhoshtqWiVcqlSIxMVG4uroKuVwu+vfvL06fPm2wmHJzc6v93O/fv98gMWkipSH2v/zbTfz7kodO2y//dpPEEHuZEBJ5tggREdWopKQETk5O+Onf7rB30K236M5tJXr6XENxcbFB+ydrY9Z9YkREJG1m3ydGRGRqlEIGpdBtdKGuxzcUJjEiIhNTOUxe13NIAZsTiYhIslgTIyIyMQpYQKFjHaXhH2FQP0xiREQmRuihT0ywT4yIiAyBfWJmpqysDLNmzUJZWZmhQ1ExxpgA44yLMWmHMWnPWOOiqjjZGf+bIGhMk/qMMSbAOONiTNphTNoz1rhqUxn3rt/bwk7Hyc6lt5UY1DXX6H8GbE4kIjIxSsig1LGhTQlp1G/YnEhERJJl8jUxpVKJq1evwsHBodrF7UpKStT+awyMMSbAOONiTNphTNpr6LiEELh9+zY8PDyqLOVTH+Y0sMPk+8QuX74MT09PQ4dBRFSrvLw8nRbBrewT2/ZbB9g56LbOX+ltBZ73O8s+MUNzcHAAADyFZ2GFRjqfb9uZ4zqfg4joYSV3lGjzxAXV9xVpz+STWGUTohUawUqmexJz1HHEDxFRdarr8qirBwM7dHwAsESaE00+iRERmRulHh47xdGJREREj5gkktiyZcvg5eUFGxsbBAcHIysry9AhEREZLYWw0MsmBUYf5ebNmxEfH4+kpCRkZ2fDz88PYWFhKCwsNHRoRERGSQkLvWxSYPRRpqam4tVXX0V0dDQ6d+6M9PR0NG7cGKtXrzZ0aEREZGBGPbCjvLwcx44dQ0JCgqrMwsICoaGhOHz4sMZjysrK1B7aaWyTKImIHjWFkEGh41Iquh7fUIy6JlZUVASFQgFXV1e1cldXV+Tn52s8Jjk5GU5OTqqNE52JyNxULoqp6yYF0oiyDhISElBcXKza8vLyDB0SEVGDUgoLvWxSYNTNic2bN4elpSUKCgrUygsKCuDm5qbxGLlcDrlc3hDhERGRgRl1qrW2tkZAQAAyMjJUZUqlEhkZGejRo4cBIyMiMl7m1Jxo1DUxAIiPj0dUVBQCAwMRFBSEtLQ0lJaWIjo62tChEREZJSV0H5ih1E8oj5zRJ7GRI0fi+vXrmDlzJvLz8+Hv74/du3dXGexBRETmx+iTGADExsYiNjbW0GEQEUmCPiYrS2WysySSGBERaU8fj43iY6eIiIgeMdbE6ijMw18v59lzNUcv5yEi+juuJ0ZERJLF5kQiIiIJYE2MiMjE6GOyMic7ExGRQSiFDEpdJzvzKfZERESPFmtiREQmRqmH5kROdiYiIoPQx1IqXIqFiIgMQgEZFDrO89L1+IYijVRLRESkAWtiREQmhs2JREQkWQro3hyo0E8oj5w0Ui0REZEGrIkREZkYNicSEZFk8QHAREREdbRs2TJ4eXnBxsYGwcHByMrKqnH/tLQ0dOzYEba2tvD09MRbb72Fe/fu1emaTGJERCZG/Hc9MV02UceBIZs3b0Z8fDySkpKQnZ0NPz8/hIWFobCwUOP+GzduxIwZM5CUlISTJ0/i008/xebNm/Huu+/W6bpMYkREJqayOVHXrS5SU1Px6quvIjo6Gp07d0Z6ejoaN26M1atXa9z/p59+QkhICMaMGQMvLy8MGDAAo0ePrrX29nfsEzMQfa0QDXCVaCJ6dEpKStRey+VyyOVytbLy8nIcO3YMCQkJqjILCwuEhobi8OHDGs/bs2dPrF+/HllZWQgKCsL58+fx7bffYty4cXWKj0mMiMjE6HMpFk9PT7XypKQkzJo1S62sqKgICoUCrq6uauWurq44deqUxvOPGTMGRUVFeOqppyCEwP379zFx4sQ6NycyiRERmRh9LoqZl5cHR0dHVfnfa2H1lZmZiXnz5mH58uUIDg7GuXPnMGXKFHz44YdITEzU+jxMYkREVC1HR0e1JKZJ8+bNYWlpiYKCArXygoICuLm5aTwmMTER48aNwyuvvAIA6NKlC0pLS/Haa6/hvffeg4WFdkmYAzuIiExMZXOirpu2rK2tERAQgIyMjP/FoFQiIyMDPXr00HjM3bt3qyQqS0tLAIAQQutrsyZGRGRilLDQeVHLuh4fHx+PqKgoBAYGIigoCGlpaSgtLUV0dDQAIDIyEi1btkRycjIAIDw8HKmpqejWrZuqOTExMRHh4eGqZKYNo05iycnJ2Lp1K06dOgVbW1v07NkTKSkp6Nixo6FDIyIyWgohg0LHgR11PX7kyJG4fv06Zs6cifz8fPj7+2P37t2qwR6XLl1Sq3m9//77kMlkeP/993HlyhW0aNEC4eHhmDt3bp2uKxN1qbc1sIEDB2LUqFHo3r077t+/j3fffRcnTpzAH3/8ATs7O63OUVJSAicnJ/TFUFjJGj3iiA2DQ+yJpK3kthLOj51HcXFxrf1PNZ7nv993bxwcDrm9bt93ZXcqsKLXVp1jetSMuia2e/dutddr166Fi4sLjh07ht69exsoKiIi46bPIfbGzqiT2N8VFxcDAJo2bVrtPmVlZSgrK1O9/vtEPSIiUyf08BR7wQcA65dSqURcXBxCQkLg6+tb7X7JyclwcnJSbX+fqEdERKZDMkksJiYGJ06cwKZNm2rcLyEhAcXFxaotLy+vgSIkIjIOCsj0skmBJJoTY2NjsXPnThw4cACtWrWqcV9Nz/UiIjInSqF7n5bSaIf8qTPqJCaEwJtvvolt27YhMzMTbdu2NXRIRERkRIw6icXExGDjxo346quv4ODggPz8fACAk5MTbG1tDRwdEZFxUuphYIeuxzcUo45yxYoVKC4uRt++feHu7q7aNm/ebOjQiIiMlq4LYlZuUmDUNTEjnodNRERGwKiTGBER1Z0hHjtlKExiREQmxpz6xJjETECYh79ezsNnMBKR1DCJERGZGCX08OxEDuwgIiJDEHoYXSiYxIiIyBDM6Sn20ui5IyIi0oA1MSIiE8PRiUREJFlsTiQiIpIA1sSIiEyMPp59yCH2RERkEGxOJCIikgDWxIiITIw51cSYxIiITIw5JTE2JxIRkWSxJkZEZGLMqSbGJEZEZGIEdB8iL/QTyiPHJEZEZGLMqSbGPjEiIpIs1sRIRV8rRANcJZrIkMypJsYkRkRkYswpibE5kYiIJIs1MSIiE2NONTEmMSIiEyOEDELHJKTr8Q2FzYlERCRZkkpi8+fPh0wmQ1xcnKFDISIyWpXriem6SYFkmhOPHDmCTz75BF27djV0KERERs2c+sQkURO7c+cOxo4di1WrVsHZ2dnQ4RARkZGQRBKLiYnB4MGDERoaWuu+ZWVlKCkpUduIiMxJ5cAOXTcpMPrmxE2bNiE7OxtHjhzRav/k5GR88MEHjzgqIiLjxeZEI5GXl4cpU6Zgw4YNsLGx0eqYhIQEFBcXq7a8vLxHHCURERmKUdfEjh07hsLCQjzxxBOqMoVCgQMHDmDp0qUoKyuDpaWl2jFyuRxyubyhQyUiMhrmNE/MqJNY//79cfz4cbWy6OhodOrUCdOnT6+SwIiI6EEC0rU5kElMDxwcHODr66tWZmdnh2bNmlUpJyKiBwQAoeOqllJZFNOo+8SIiIhqYtQ1MU0yMzMNHQIRkVFTQgaZjk/c4BM7iIjIIMxpYAebE4mISLJYE6NHIszDX2/n2nM1R2/nIjIHSiGDzEwmOzOJERGZGCH0MDpRIsMT2ZxIRESSxZoYEZGJMaeBHUxiREQmxpySGJsTiYhIslgTIyIyMRydSEREksXRiURERBLAmhgRkYl5UBPTdWCHnoJ5xJjEiIhMjDmNTmQSIyIyMQK6rwcmkYoY+8SIiEi6WBMjIjIxbE4kIiLpMqP2RDYnEhGRXixbtgxeXl6wsbFBcHAwsrKyatz/1q1biImJgbu7O+RyOR577DF8++23dboma2JERKZGD82JqOPxmzdvRnx8PNLT0xEcHIy0tDSEhYXh9OnTcHFxqbJ/eXk5nnnmGbi4uGDLli1o2bIlLl68iCZNmtTpukxiREQmxhBP7EhNTcWrr76K6OhoAEB6ejq++eYbrF69GjNmzKiy/+rVq3Hjxg389NNPaNSoEQDAy8urznGyOZGIiKpVUlKitpWVlVXZp7y8HMeOHUNoaKiqzMLCAqGhoTh8+LDG8+7YsQM9evRATEwMXF1d4evri3nz5kGhUNQpPtbEyOiFefjr5Tx7rubo5TxExk6foxM9PT3VypOSkjBr1iy1sqKiIigUCri6uqqVu7q64tSpUxrPf/78eXz//fcYO3Ysvv32W5w7dw6TJk1CRUUFkpKStI6TSYyIyNQIWZ37tDSeA0BeXh4cHR1VxXK5XLfz/pdSqYSLiwtWrlwJS0tLBAQE4MqVK1i4cCGTGBER6Yejo6NaEtOkefPmsLS0REFBgVp5QUEB3NzcNB7j7u6ORo0awdLSUlX2+OOPIz8/H+Xl5bC2ttYqPvaJERGZmMqBHbpu2rK2tkZAQAAyMjJUZUqlEhkZGejRo4fGY0JCQnDu3DkolUpV2ZkzZ+Du7q51AgOYxIiITI/Q01YH8fHxWLVqFdatW4eTJ0/ijTfeQGlpqWq0YmRkJBISElT7v/HGG7hx4wamTJmCM2fO4JtvvsG8efMQExNTp+safRK7cuUKXnrpJTRr1gy2trbo0qULjh49auiwiIjoISNHjsSiRYswc+ZM+Pv7IycnB7t371YN9rh06RKuXbum2t/T0xN79uzBkSNH0LVrV0yePBlTpkzROBy/JkbdJ3bz5k2EhISgX79+2LVrF1q0aIGzZ8/C2dnZ0KERERktQz07MTY2FrGxsRrfy8zMrFLWo0cP/Pzzz3W+zsOMOomlpKTA09MTa9asUZW1bdvWgBEREUmERJ59qCujbk7csWMHAgMD8eKLL8LFxQXdunXDqlWrDB0WEZFRq6yJ6bpJgVEnsfPnz2PFihXo0KED9uzZgzfeeAOTJ0/GunXrqj2mrKysygxzIiIyTUbdnKhUKhEYGIh58+YBALp164YTJ04gPT0dUVFRGo9JTk7GBx980JBhEhEZFy7FYhzc3d3RuXNntbLHH38cly5dqvaYhIQEFBcXq7a8vLxHHSYRkZGR6WkzfkZdEwsJCcHp06fVys6cOYM2bdpUe4xcLtfbY1GIiMi4GXVN7K233sLPP/+MefPm4dy5c9i4cSNWrlxZ58lwRERmxQCTnQ3FqJNY9+7dsW3bNnz++efw9fXFhx9+iLS0NIwdO9bQoRERGS8zSmJG3ZwIAEOGDMGQIUMMHQYRERmhetXE8vLycPnyZdXrrKwsxMXFYeXKlXoLjIiI6qlyKRZdNwmoVxIbM2YM9u/fDwDIz8/HM888g6ysLLz33nuYPXu2XgMkIqK6aein2BtSvZoTT5w4gaCgIADAF198AV9fX/z444/47rvvMHHiRMycOVOvQRLpg75WiAa4SjSRsahXEquoqFANY9+3bx+ee+45AECnTp3UnlJMREQGwMnONfPx8UF6ejoOHjyIvXv3YuDAgQCAq1evolmzZnoNkIiI6oh9YjVLSUnBJ598gr59+2L06NHw8/MD8OCBvZXNjERERI9avZoT+/bti6KiIpSUlKit7fXaa6+hcePGeguOiIjqTiYebLqeQwrqVRNLSkrC5cuXqyxO6eXlBRcXF70ERkRE9WRGk53rlcS++uoreHt7o3///ti4cSPKysr0HRcREdUX+8RqlpOTgyNHjsDHxwdTpkyBm5sb3njjDRw5ckTf8REREVWr3s9O7NatGz766CNcvXoVn376KS5fvoyQkBB07doVS5YsQXFxsT7jJCIibbE5UXtCCFRUVKC8vBxCCDg7O2Pp0qXw9PTE5s2b9REjERHVBZNY7Y4dO4bY2Fi4u7vjrbfeQrdu3XDy5En88MMPOHv2LObOnYvJkyfrM1YiIiI19UpiXbp0wZNPPonc3Fx8+umnyMvLw/z589G+fXvVPqNHj8b169f1FigREWnJjGpi9ZonFhERgfHjx6Nly5bV7tO8eXMolcp6B0ZERPWkj9GFpjo6saKiAmvXrkVJScmjiIeIiEhrda6JNWrUCPfu3XsUsRARkR7wiR21iImJQUpKCu7fv6/veIiISFfsE6vZkSNHkJGRge+++w5dunSBnZ2d2vtbt27VS3BEREQ1qVcSa9KkCUaMGKHvWIiIiOqkXklszZo1+o6DiIj0RAY99InpJZJHr15JDADu37+PzMxM/PnnnxgzZgwcHBxw9epVODo6wt7eXp8xEhmdMA9/vZ1rz9UcvZ2LyNzUK4ldvHgRAwcOxKVLl1BWVoZnnnkGDg4OSElJQVlZGdLT0/UdJxERaYvzxGo2ZcoUBAYG4ubNm7C1tVWVP//888jIyNBbcEREVA8cnVizgwcP4qeffoK1tbVauZeXF65cuaKXwIiIqJ70kYQkksTqVRNTKpVQKBRVyi9fvgwHBwedgyIiItJGvZLYgAEDkJaWpnotk8lw584dJCUl4dlnn9VXbEREVA+VT+zQdZOCeiWxxYsX48cff0Tnzp1x7949jBkzRtWUmJKSorfgFAoFEhMT0bZtW9ja2sLb2xsffvghhJDIT5eIyBDYJ1azVq1a4bfffsOmTZvw+++/486dO5gwYQLGjh2rNtBDVykpKVixYgXWrVsHHx8fHD16FNHR0XBycuJaZUREVP95YlZWVnjppZf0GUsVP/30E4YOHYrBgwcDeDBw5PPPP0dWVtYjvS4RkaSZ0cCOeiWxzz77rMb3IyMj6xXM3/Xs2RMrV67EmTNn8Nhjj+G3337DoUOHkJqaWu0xZWVlKCsrU73mkjFEZG7M6Sn29UpiU6ZMUXtdUVGBu3fvwtraGo0bN9ZbEpsxYwZKSkrQqVMnWFpaQqFQYO7cuRg7dmy1xyQnJ+ODDz7Qy/WJiMi41Wtgx82bN9W2O3fu4PTp03jqqafw+eef6y24L774Ahs2bMDGjRuRnZ2NdevWYdGiRVi3bl21xyQkJKC4uFi15eXl6S0eIiJJqHxih66bBNS7T+zvOnTogPnz5+Oll17CqVOn9HLOadOmYcaMGRg1ahQAoEuXLrh48SKSk5MRFRWl8Ri5XA65XK6X6xMRSZIZ9YnVqyZWHSsrK1y9elVv57t79y4sLNRDtLS0hFKp1Ns1iIhIuupVE9uxY4faayEErl27hqVLlyIkJEQvgQFAeHg45s6di9atW8PHxwe//vorUlNTMX78eL1dg4jI1HBgRy2GDRum9lomk6FFixZ4+umnsXjxYn3EBQD4+OOPkZiYiEmTJqGwsBAeHh54/fXXMXPmTL1dg4jI5JhRc2K9klhDNec5ODggLS1N7RFXRERUC308NsqUk1h8fLzW+9Y0p4uIiEgX9Upiv/76K7Kzs3H//n107NgRAHDmzBlYWlriiSeeUO0nk0ljiCYRkUlhc2LNwsPD4eDggHXr1sHZ2RnAg7lj0dHR6NWrF95++229BklkysI8/PVynj1Xc/RyHjIBZpTE6v0U++TkZFUCAwBnZ2fMmTNHrwM7iIiIalKvmlhJSQmuX79epfz69eu4ffu2zkEREVH9mdMQ+3rVxJ5//nlER0dj69atuHz5Mi5fvowvv/wSEyZMwPDhw/UdIxERkUb1qomlp6dj6tSpGDNmDCoqKh6cyMoKEyZMwMKFC/UaIBERUXXqlcQaN26M5cuXY+HChfjzzz8BAN7e3rCzs9NrcEREVA9mNLBDpwcA29nZoWvXrvqKhYiI9IB9YkRERBKgt6VYiIjIiEikJqUrJjEiIlNjRn1ibE4kIiLJYk2MiMjEmNPADiYxIiJTY0bNiUxiREQmxpxqYuwTIyIiyWISIyIyNUJPWx0tW7YMXl5esLGxQXBwMLKysrQ6btOmTZDJZBg2bFidr8kkRkRkagyQxDZv3oz4+HgkJSUhOzsbfn5+CAsLQ2FhYY3HXbhwAVOnTkWvXr3qdsH/YhIjIiKdpaam4tVXX0V0dDQ6d+6M9PR0NG7cGKtXr672GIVCgbFjx+KDDz5Au3bt6nVdDuwgMhH6WiEa4CrRUqfPgR0lJSVq5XK5HHK5XK2svLwcx44dQ0JCgqrMwsICoaGhOHz4cLXXmD17NlxcXDBhwgQcPHiwXnGyJkZEZGr02Jzo6ekJJycn1ZacnFzlckVFRVAoFHB1dVUrd3V1RX5+vsYQDx06hE8//RSrVq3S6VZZEyMiomrl5eXB0dFR9frvtbD6uH37NsaNG4dVq1ahefPmOp2LSYyIyNTocbKzo6OjWhLTpHnz5rC0tERBQYFaeUFBAdzc3Krs/+eff+LChQsIDw9XlSmVSgAPFlg+ffo0vL29tQqTzYlERCamsk9M101b1tbWCAgIQEZGhqpMqVQiIyMDPXr0qLJ/p06dcPz4ceTk5Ki25557Dv369UNOTg48PT21vjZrYkREpLP4+HhERUUhMDAQQUFBSEtLQ2lpKaKjowEAkZGRaNmyJZKTk2FjYwNfX1+145s0aQIAVcprY9Ca2IEDBxAeHg4PDw/IZDJs375d7X0hBGbOnAl3d3fY2toiNDQUZ8+eNUywRERSYYB5YiNHjsSiRYswc+ZM+Pv7IycnB7t371YN9rh06RKuXbum+739jUFrYqWlpfDz88P48eMxfPjwKu8vWLAAH330EdatW4e2bdsiMTERYWFh+OOPP2BjY2OAiImIjJ+hnp0YGxuL2NhYje9lZmbWeOzatWvrfkEYOIkNGjQIgwYN0vieEAJpaWl4//33MXToUADAZ599BldXV2zfvh2jRo1qyFCJiMgIGe3AjtzcXOTn5yM0NFRV5uTkhODg4Bonz5WVlaGkpERtIyIyKwZ6dqIhGG0Sq5wgV5fJcwCQnJysNjGvLqNciIhMApOYdCUkJKC4uFi15eXlGTokIqIGJdPTJgVGm8QqJ8hpO3muklwuV03O02aSHhERSZfRJrG2bdvCzc1NbfJcSUkJfvnlF42T54iI6L/MqDnRoKMT79y5g3Pnzqle5+bmIicnB02bNkXr1q0RFxeHOXPmoEOHDqoh9h4eHvVaOI2IyFwYaoi9IRg0iR09ehT9+vVTvY6PjwcAREVFYe3atXjnnXdQWlqK1157Dbdu3cJTTz2F3bt3c44YEREBMHAS69u3L4SoPt3LZDLMnj0bs2fPbsCoiIgkTo8PADZ2fHYiEZEpkkgS0pXRDuwgIiKqDWtiRFRFmIe/Xs6z52qOXs5DdcOBHUREJF1m1CfG5kQiIpIs1sSIiEwMmxOJiEi62JxIRERk/FgTIyIyMWxOJCIi6TKj5kQmMSIiU2NGSYx9YkREJFmsiRERmRj2iRERkXSxOZGIiMj4sSZGRGRiZEJAVsNajdqeQwqYxIiITA2bE4mIiIwfa2JERCaGoxOJiEi6zKg5kUmMiB4Zfa0QDXCVaNKMSYyIyMSwOZGIiKTLjJoTOTqRiIgkizUxIiITw+ZEIiKSLjYnNowDBw4gPDwcHh4ekMlk2L59u+q9iooKTJ8+HV26dIGdnR08PDwQGRmJq1evGi5gIiKJqKyN1XeTCoMmsdLSUvj5+WHZsmVV3rt79y6ys7ORmJiI7OxsbN26FadPn8Zzzz1ngEiJiMgYGbQ5cdCgQRg0aJDG95ycnLB37161sqVLlyIoKAiXLl1C69atGyJEIiLpEeLBpus5JEBSfWLFxcWQyWRo0qRJtfuUlZWhrKxM9bqkpKQBIiMiMh7mNLBDMkPs7927h+nTp2P06NFwdHSsdr/k5GQ4OTmpNk9PzwaMkoiIGpIkklhFRQUiIiIghMCKFStq3DchIQHFxcWqLS8vr4GiJCIyEkJPmwQYfXNiZQK7ePEivv/++xprYQAgl8shl8sbKDoiIuMjUz7YdD2HFBh1EqtMYGfPnsX+/fvRrFkzQ4dERERGxKBJ7M6dOzh37pzqdW5uLnJyctC0aVO4u7vjhRdeQHZ2Nnbu3AmFQoH8/HwAQNOmTWFtbW2osImIjJsZTXY2aBI7evQo+vXrp3odHx8PAIiKisKsWbOwY8cOAIC/v7/acfv370ffvn0bKkwiIkkxp9GJBk1iffv2hahhLkJN7xERERl1nxgREdUDJzsTEZFUsTmRiMjIhHn46+1ce67m6O1cZFhMYkREpoajE4mISKrYnEhERNJlRgM7JPHsRCIiIk1YEyMiMjFsTiQiIukyo4EdbE4kIiLJYk2MiMjEsDmRiIikSykebLqeQwLYnEhERJLFmhgRkakxo4EdTGJERCZGBj30ieklkkePzYlERCRZrIkREZkaPnaKiIikqnKIva5bXS1btgxeXl6wsbFBcHAwsrKyqt131apV6NWrF5ydneHs7IzQ0NAa968OkxgRkakRetrqYPPmzYiPj0dSUhKys7Ph5+eHsLAwFBYWatw/MzMTo0ePxv79+3H48GF4enpiwIABuHLlSp2uyyRGREQ6S01Nxauvvoro6Gh07twZ6enpaNy4MVavXq1x/w0bNmDSpEnw9/dHp06d8M9//hNKpRIZGRl1ui6TGBGRiZEJoZcNAEpKStS2srKyKtcrLy/HsWPHEBoaqiqzsLBAaGgoDh8+rFXMd+/eRUVFBZo2bVqne+XADiIyO2Ee/no5z56rOXo5j94p/7vpeg4Anp6easVJSUmYNWuWWllRUREUCgVcXV3Vyl1dXXHq1CmtLjd9+nR4eHioJUJtMIkREVG18vLy4OjoqHotl8v1fo358+dj06ZNyMzMhI2NTZ2OZRIjIjIxDzcH6nIOAHB0dFRLYpo0b94clpaWKCgoUCsvKCiAm5tbjccuWrQI8+fPx759+9C1a9c6x8k+MSIiU9PAoxOtra0REBCgNiijcpBGjx49qj1uwYIF+PDDD7F7924EBgbW4Qb/hzUxIiLSWXx8PKKiohAYGIigoCCkpaWhtLQU0dHRAIDIyEi0bNkSycnJAICUlBTMnDkTGzduhJeXF/Lz8wEA9vb2sLe31/q6Bq2JHThwAOHh4fDw8IBMJsP27dur3XfixImQyWRIS0trsPiIiCSp8okdum51MHLkSCxatAgzZ86Ev78/cnJysHv3btVgj0uXLuHatWuq/VesWIHy8nK88MILcHd3V22LFi2q03UNWhMrLS2Fn58fxo8fj+HDh1e737Zt2/Dzzz/Dw8OjAaMjIpImQy2KGRsbi9jYWI3vZWZmqr2+cOFC3S+ggUGT2KBBgzBo0KAa97ly5QrefPNN7NmzB4MHD26gyIiISAqMuk9MqVRi3LhxmDZtGnx8fLQ6pqysTG0yXklJyaMKj4jIOPEBwMYhJSUFVlZWmDx5stbHJCcnw8nJSbX9faIeEZGpkyn1s0mB0SaxY8eOYcmSJVi7di1kMu2XZ0tISEBxcbFqy8vLe4RREhGRIRltEjt48CAKCwvRunVrWFlZwcrKChcvXsTbb78NLy+vao+Ty+WqyXnaTNIjIjI5BhidaChG2yc2bty4Ks/QCgsLw7hx41TzDoiISIN6LKWi8RwSYNAkdufOHZw7d071Ojc3Fzk5OWjatClat26NZs2aqe3fqFEjuLm5oWPHjg0dKhGRZOjzsVPGzqBJ7OjRo+jXr5/qdXx8PAAgKioKa9euNVBUREQkFQZNYn379oWoQ7bX1+Q4IiKTZkZD7I22T4yIiOpJQPf1xKSRw4x3dCIREVFtWBMjIqonfa0QfV9UADivl3MBHNhBRERSJqCHPjG9RPLIsTmRiIgkizUxIiJTw9GJREQkWUoA2j9ytvpzSACbE4mISLJYEyMiMjEcnUhERNJlRn1ibE4kIiLJYk2MiMjUmFFNjEmMiMjUMIkREZFkcYg9ERGR8WNNjIjIxHCIPRERSZcZ9YmxOZGIiCSLNTEiIlOjFIBMx5qUUho1MSYxIiJTY0bNiSafxMR/fxH3USGZRd6IyLzcRwWA/31fkfZMPondvn0bAHAI3xo4EiKimt2+fRtOTk56OJMeamIS+avf5JOYh4cH8vLy4ODgAJlM8+y/kpISeHp6Ii8vD46Ojg0coWbGGBNgnHExJu0wJu01dFxCCNy+fRseHh76OiGbE02FhYUFWrVqpdW+jo6ORvU/EmCcMQHGGRdj0g5j0l5DxqWfGpj5MfkkRkRkdpQCOjcHcnQiEREZhFA+2HQ9hwRwsjMAuVyOpKQkyOVyQ4eiYowxAcYZF2PSDmPSnrHGRVXJBMd0EhGZhJKSEjg5OSHU8w1YWeiWgO8ry7AvbwWKi4uNsr+yEpsTiYhMDfvEiIhIssxoiD37xIiISLJYEyMiMjUCeqiJ6SWSR441MTJbffv2RVxcnKHDINK/yuZEXTcJYBIjIiLJYnMiEZGpUSoB6DhZWcnJzkSS8s0338DJyQkbNmxAXl4eIiIi0KRJEzRt2hRDhw7FhQsXAAAHDhxAo0aNkJ+fr3Z8XFwcevXqBQC4ePEiwsPD4ezsDDs7O/j4+ODbb7mSAjUQNicSmZeNGzdi9OjR2LBhAyIiIhAWFgYHBwccPHgQP/74I+zt7TFw4ECUl5ejd+/eaNeuHf7v//5PdXxFRQU2bNiA8ePHAwBiYmJQVlaGAwcO4Pjx40hJSYG9vb2hbo/IZLE5kczesmXL8N577+Hrr79Gnz59sH79eiiVSvzzn/9ULd+zZs0aNGnSBJmZmRgwYAAmTJiANWvWYNq0aQCAr7/+Gvfu3UNERAQA4NKlSxgxYgS6dOkCAGjXrp1hbo7MkxnNE2MSI7O2ZcsWFBYW4scff0T37t0BAL/99hvOnTsHBwcHtX3v3buHP//8EwDw8ssv4/3338fPP/+MJ598EmvXrkVERATs7OwAAJMnT8Ybb7yB7777DqGhoRgxYgS6du3asDdH5suMntjB5kQya926dUOLFi2wevVq1dLwd+7cQUBAAHJyctS2M2fOYMyYMQAAFxcXhIeHY82aNSgoKMCuXbtUTYkA8Morr+D8+fMYN24cjh8/jsDAQHz88ccGuUciU8aaGJk1b29vLF68GH379oWlpSWWLl2KJ554Aps3b4aLi0uNDz595ZVXMHr0aLRq1Qre3t4ICQlRe9/T0xMTJ07ExIkTkZCQgFWrVuHNN9981LdEBCGUEDoupaLr8Q2FNTEye4899hj279+PL7/8EnFxcRg7diyaN2+OoUOH4uDBg8jNzUVmZiYmT56My5cvq44LCwuDo6Mj5syZg+joaLVzxsXFYc+ePcjNzUV2djb279+Pxx9/vKFvjcyVEA+aA3XZ2CdGJB0dO3bE999/r6qRHThwANOnT8fw4cNx+/ZttGzZEv3791ermVlYWODll1/GvHnzEBkZqXY+hUKBmJgYXL58GY6Ojhg4cCD+8Y9/NPRtEZk8ridGpIMJEybg+vXr2LFjh6FDIVKtJ9bfaRysZNY6neu+KEdG8f9xPTEiU1RcXIzjx49j48aNTGBkfJRKQKZjn5ZE+sSYxIjqYejQocjKysLEiRPxzDPPGDocInVCD0PsJdJIxyRGVA+ZmZmGDoGIwCRGRGRyhFIJoWNzolSG2DOJERGZGjNqTuQ8MSIikizWxIiITI1SADLzqIkxiRERmRohoPOimBJJYmxOJCIiyWJNjIjIxAilgNCxOVEqD3NiTYyIyNQIpX62Olq2bBm8vLxgY2OD4OBgZGVl1bj/v/71L3Tq1Ak2Njbo0qULvv322zpfk0mMiIh0tnnzZsTHxyMpKQnZ2dnw8/NDWFgYCgsLNe7/008/YfTo0ZgwYQJ+/fVXDBs2DMOGDcOJEyfqdF0+AJiIyERUPgC4r+x5WMka6XSu+6ICmWKb1g8ADg4ORvfu3bF06VIAgFKphKenJ958803MmDGjyv4jR45EaWkpdu7cqSp78skn4e/vj/T0dK3jZE2MiMjUNHBzYnl5OY4dO4bQ0FBVmYWFBUJDQ3H48GGNxxw+fFhtf+DBGn3V7V8dDuwgIjIx91Gh8wM77qMCwIPa3cPkcjnkcrlaWVFRERQKBVxdXdXKXV1dcerUKY3nz8/P17h/fn5+neJkEiMiMhHW1tZwc3PDofy6D5DQxN7eHp6enmplSUlJmDVrll7Orw9MYkREJsLGxga5ubkoLy/Xy/mEEJDJZGplf6+FAUDz5s1haWmJgoICtfKCggK4ublpPLebm1ud9q8OkxgRkQmxsbGBjY1Ng17T2toaAQEByMjIwLBhwwA8GNiRkZGB2NhYjcf06NEDGRkZiIuLU5Xt3bsXPXr0qNO1mcSIiEhn8fHxiIqKQmBgIIKCgpCWlobS0lJER0cDACIjI9GyZUskJycDAKZMmYI+ffpg8eLFGDx4MDZt2oSjR49i5cqVdboukxgREels5MiRuH79OmbOnIn8/Hz4+/tj9+7dqsEbly5dgoXF/wbE9+zZExs3bsT777+Pd999Fx06dMD27dvh6+tbp+tynhgREUkW54kREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFk/T/Qcz0oCgnhnwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:32.647017Z",
     "iopub.status.busy": "2025-02-07T13:59:32.646784Z",
     "iopub.status.idle": "2025-02-07T13:59:33.170083Z",
     "shell.execute_reply": "2025-02-07T13:59:33.169374Z",
     "shell.execute_reply.started": "2025-02-07T13:59:32.646995Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.171353Z",
     "iopub.status.busy": "2025-02-07T13:59:33.170980Z",
     "iopub.status.idle": "2025-02-07T13:59:33.197616Z",
     "shell.execute_reply": "2025-02-07T13:59:33.196919Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.171326Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.198734Z",
     "iopub.status.busy": "2025-02-07T13:59:33.198448Z",
     "iopub.status.idle": "2025-02-07T13:59:33.204795Z",
     "shell.execute_reply": "2025-02-07T13:59:33.204120Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.198709Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.205757Z",
     "iopub.status.busy": "2025-02-07T13:59:33.205533Z",
     "iopub.status.idle": "2025-02-07T13:59:33.214641Z",
     "shell.execute_reply": "2025-02-07T13:59:33.214077Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.205733Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.216010Z",
     "iopub.status.busy": "2025-02-07T13:59:33.215481Z",
     "iopub.status.idle": "2025-02-07T13:59:33.330706Z",
     "shell.execute_reply": "2025-02-07T13:59:33.329989Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.215975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.332029Z",
     "iopub.status.busy": "2025-02-07T13:59:33.331622Z",
     "iopub.status.idle": "2025-02-07T13:59:33.337139Z",
     "shell.execute_reply": "2025-02-07T13:59:33.336357Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.331995Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.338496Z",
     "iopub.status.busy": "2025-02-07T13:59:33.338004Z",
     "iopub.status.idle": "2025-02-07T13:59:33.344916Z",
     "shell.execute_reply": "2025-02-07T13:59:33.344220Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.338462Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"dropout_30.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.346459Z",
     "iopub.status.busy": "2025-02-07T13:59:33.345861Z",
     "iopub.status.idle": "2025-02-07T13:59:33.351755Z",
     "shell.execute_reply": "2025-02-07T13:59:33.351148Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.346425Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.353025Z",
     "iopub.status.busy": "2025-02-07T13:59:33.352503Z",
     "iopub.status.idle": "2025-02-07T13:59:33.358544Z",
     "shell.execute_reply": "2025-02-07T13:59:33.357921Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.352990Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.359780Z",
     "iopub.status.busy": "2025-02-07T13:59:33.359476Z",
     "iopub.status.idle": "2025-02-07T13:59:33.369537Z",
     "shell.execute_reply": "2025-02-07T13:59:33.368864Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.359747Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.370983Z",
     "iopub.status.busy": "2025-02-07T13:59:33.370418Z",
     "iopub.status.idle": "2025-02-07T13:59:33.734475Z",
     "shell.execute_reply": "2025-02-07T13:59:33.733918Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.370947Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.3, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 8, # 可调整\n",
    "    \"num_decoder_layers\": 6, # 可调整\n",
    "    \"num_encoder_layers\": 6, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:33.735775Z",
     "iopub.status.busy": "2025-02-07T13:59:33.735186Z",
     "iopub.status.idle": "2025-02-07T13:59:34.722867Z",
     "shell.execute_reply": "2025-02-07T13:59:34.722283Z",
     "shell.execute_reply.started": "2025-02-07T13:59:33.735739Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 71938239\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:59:34.723807Z",
     "iopub.status.busy": "2025-02-07T13:59:34.723585Z",
     "iopub.status.idle": "2025-02-07T14:57:39.198917Z",
     "shell.execute_reply": "2025-02-07T14:57:39.198381Z",
     "shell.execute_reply.started": "2025-02-07T13:59:34.723784Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "83499it [58:01, 23.98it/s, epoch=79, loss=2.93, val_loss=3.48]                        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 80 / global_step 83500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T14:57:39.200482Z",
     "iopub.status.busy": "2025-02-07T14:57:39.199835Z",
     "iopub.status.idle": "2025-02-07T14:57:39.354674Z",
     "shell.execute_reply": "2025-02-07T14:57:39.354146Z",
     "shell.execute_reply.started": "2025-02-07T14:57:39.200458Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/dropout_30.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T14:57:39.355606Z",
     "iopub.status.busy": "2025-02-07T14:57:39.355304Z",
     "iopub.status.idle": "2025-02-07T14:57:50.857876Z",
     "shell.execute_reply": "2025-02-07T14:57:50.857228Z",
     "shell.execute_reply.started": "2025-02-07T14:57:39.355585Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "18it [00:00, 88.68it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:09, 98.54it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 3.3438341584264863\n",
      "testing bleu: 0.5786188708376214\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T14:57:50.859369Z",
     "iopub.status.busy": "2025-02-07T14:57:50.858689Z",
     "iopub.status.idle": "2025-02-07T14:57:50.862019Z",
     "shell.execute_reply": "2025-02-07T14:57:50.861441Z",
     "shell.execute_reply.started": "2025-02-07T14:57:50.859333Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T14:57:50.862997Z",
     "iopub.status.busy": "2025-02-07T14:57:50.862707Z",
     "iopub.status.idle": "2025-02-07T14:57:51.236561Z",
     "shell.execute_reply": "2025-02-07T14:57:51.236035Z",
     "shell.execute_reply.started": "2025-02-07T14:57:50.862963Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T14:57:51.237453Z",
     "iopub.status.busy": "2025-02-07T14:57:51.237193Z",
     "iopub.status.idle": "2025-02-07T14:57:52.524763Z",
     "shell.execute_reply": "2025-02-07T14:57:52.524227Z",
     "shell.execute_reply.started": "2025-02-07T14:57:51.237431Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  9%|▊         | 11/128 [00:00<00:01, 90.39it/s] \n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['man in white boat on a boat on a lake.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake.\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach.\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race.\n",
    "    # \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
